About this Research Topic
This research topic aims to tackle cellular adversities caused by chemical exposure and viral infection using quantitative modelling approaches. Such approaches are expected to leverage mechanism-based modelling by explicitly taking cellular metabolism, cellular stress responses, cell signalling and/or immune networks into account. Such approaches enable the dissection of the spatiotemporal complexities through model simulation and analysis. Integration of modelling and data could further quantitatively characterize cellular adversities through parameter estimation and uncertainty quantification. Modelling techniques can include ordinary differential equations (ODEs)-based deterministic modeling,stochastic modelling, agent-based modelling, and other approaches. Recent advances such as scientific machine learning, including neural ODEs and surrogate modelling may speed up model development and data integration. Overall, quantitative modelling approaches based on a mechanistic understanding represent a promising tool to predict cellular adversities and related pathogenesis, which is in line with the Replacement, Reduction and Refinement (3Rs) paradigm shift restricting animal use in chemical risk assessment and in drug discovery and development.
We welcome allarticle types, including but not limited to the following:
• Quantitative modelling of how cells respond to chemical exposure leading to cellular adversities depending on the dose and duration of exposure, and particularly based on a mechanistic understanding with identification of key events modulating the response landscape.
• Quantitative modelling of how viral infection leads to cellular adversities including viral load dynamics and cellular immune responses, based on a mechanistic understanding of the processes involved.
• Multiscale modelling considering cellular adversities and tissue damage are also encouraged. Studies should address how chemical exposure or viral infection leads to cellular adversities as well as the interplay across biological organization levels (i.e., molecular, cellular and tissue level).
• Quantitative modelling approaches that integrate published or novel experimental data are particularly welcome. Integration based on multiple types of data (e.g. from in vitro assays, in vivo measurements, or clinical data) is encouraged.
• Efforts of data curation related to cellular adversities showcasing a demonstrable interface for quantitative modelling are also welcome.
Keywords: modeling, cellular adversity, chemical insult, viral infection, Cellular adversities, quantitative systems pharmacology, quantitative systems toxicology, systems biology, chemical risk assessment, modelling of pathogenesis, viral infection dynamics, immune response
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.